Farm Operations Research Articles

Influence of the sound propagation models to predict far field wind turbine noise

During the development process of a wind farm, the noise impact on neighboring residents is of crucial importance to limit annoyance and comply with local regulations. In France, this assessment is carried out by modelling the propagation of sound from each wind turbine to residents. For wind farm owners, this phase will in fine define the operation of the wind farm and therefore its electricity production. However, there are major concerns and uncertainties during this modelling phase. A concrete example of the influence of the choice of propagation model on the production of a wind farm is presented, giving an idea of its economic impact. Comparisons of the impact of wind farm noise modelled by engineering models with measurements taken on wind farms in operation provide a better understanding of the choice of certain methods or parameters. Taken together, these observations suggest areas for further research and development, with the aim of perhaps defining a more restricted framework for the sound propagation modelling stage, in order to help operators predict wind farm noise more accurately.

Methods for addressing variable turbine operations during wind farm noise compliance monitoring

Post-construction wind farm noise measurements at dwelling locations are commonly required in Australia to verify compliance with noise limits. These are often required as soon as the wind farm begins operating. However, commissioning issues and electricity network restrictions are a common occurrence during initial stages of operation. As such, curtailed modes of operation and shutdowns are a regular occurrence, and large sections of a wind farm may only be operating intermittently. This presents challenges to obtaining noise measurement data that is representative of normal operation of the wind farm, and may ultimately limit the conclusions that are able to be reached from measurements during the early stages of operation. To address these challenges, it is necessary to identify and account for any periods when operational noise levels may be lower as a result of turbines being curtailed or shut down. Several methods have been developed for this purpose, and range from simple procedures that remove any data affected by curtailment or shutdown, through to more complex assessments of the magnitude and implications of the uncertainty introduced by variable wind turbine operations. These methods are presented and assessed in this paper with case studies of their application to operational projects.

Contract Farming & Cost of Funding: Evidence from Madurase Solar Salt Small Businesses

This study aimed to estimate the indirect expenses borne by tenant farmers due to credits they receive in community salt farming operations managed through contract farming systems in Pamekasan Regency. Credits constitute a crucial aspect of tenant farmers’ rights within the community salt farming contract farming framework. This study employed a comparative analysis of the cost of funds based on the adopted contract farming models, specifically the two-way and three-way contract farming structures. Moreover, it investigated the correlation between the cultivated salt field’s size and the magnitude of the cost of funds endured by tenant farmers. The results of the analysis reveal that the average costs of funds borne by tenant farmers are notably high, ranging from 5.24% to 6.71% per month. Surprisingly, these figures exceed the loan interest rates offered by two formal financial institutions: BRI and Bank JATIM. Another significant finding is the positive correlation between the size of cultivated salt fields and the magnitude of the cost of funds borne by tenant farmers. Lastly, there is a clear positive relationship between the credit amount received and the corresponding cost of funds endured by tenant farmers. The substantial costs of funds shouldered by tenant farmers involved in the community salt farming, operated through contract farming systems in Pamekasan Regency, signify inefficiencies within the contract farming structure. It is expected that the government can aid these tenant farmers by disbursing subsidized credits through collaborations with relevant ministries and local formal financial institutions.

Assessing the spatial variability of cage movement and velocity attenuation of an off-bottom oyster farm and its influence on eastern oyster (Crassostrea virginica) growth performance

Assessing the spatial variability of cage movement and velocity attenuation of an off-bottom oyster farm and its influence on eastern oyster (Crassostrea virginica) growth performance

Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression

The rapid expansion of wind power worldwide underscores the critical significance of engineering-focused analytical wake models in both the design and operation of wind farms. These theoretically derived analytical wake models have limited predictive capabilities, particularly in the near-wake region close to the turbine rotor, due to assumptions that do not hold. Knowledge discovery methods can bridge these gaps by extracting insights, adjusting for theoretical assumptions, and developing accurate models for physical processes. In this study, we introduce a genetic symbolic regression (SR) algorithm to discover an interpretable mathematical expression for the mean velocity deficit throughout the wake, a previously unavailable insight. By incorporating a double Gaussian distribution into the SR algorithm as domain knowledge and designing a hierarchical equation structure, the search space is reduced, thus efficiently finding a concise, physically informed, and robust wake model. The proposed mathematical expression (equation) can predict the wake velocity deficit at any location in the full-wake region with high precision and stability. The model's effectiveness and practicality are validated through experimental data and high-fidelity numerical simulations.

RUL forecasting for wind turbine predictive maintenance based on deep learning

Predictive maintenance (PdM) is increasingly pursued to reduce wind farm operation and maintenance costs by accurately predicting the remaining useful life (RUL) and strategically scheduling maintenance. However, the remoteness of wind farms often renders current methodologies ineffective, as they fail to provide a sufficiently reliable advance time window for maintenance planning, limiting PdM's practicality. This study introduces a novel deep learning (DL) methodology for future-RUL forecasting. By employing a multi-parametric attention-based DL approach that bypasses feature engineering, thereby minimizing the risk of human error, two models-ForeNet-2d and ForeNet-3d-are proposed. These models successfully forecast the RUL for seven multifaceted wind turbine (WT) failures with a 2-week forecast window. The most precise forecast deviated by only 10 minutes from the actual RUL, while the least accurate prediction deviated by 1.8 days, with most predictions being off by only a few hours. This methodology offers a substantial time frame to access remote WTs and perform necessary maintenance, thereby enabling the practical implementation of PdM.

Analysis of Influence of Grid-Following and Grid-Forming Static Var Generators on High-Frequency Resonance in Doubly Fed Induction Generator-Based Wind Farms

In Doubly Fed Induction Generator (DFIG)-based wind farms with Static Var Generators (SVGs), high-frequency resonance will be more like to occur when an unloaded cable is put into operation, which will threaten the stable operation of the wind farm. To address this issue, the influence of power outer loops on the impedance of grid-connected inverters is considered. Based on harmonic linearization, theoretical models for the sequence impedances of DFIGs, Grid-following (GFL) SVGs, and Grid-forming (GFM) SVGs are established. The correctness of the three models is verified by impedance scanning using the frequency sweep method. Through a comparative analysis of these sequence impedances, it is found that unlike the GFM SVG (which exhibits inductive impedance), the GFL SVG exhibits capacitive impedance in the high-frequency band, which leads to negative damping characteristics in the high-frequency band for the wind farm system with the grid-following SVG; thereby, the risk of high-frequency resonance also increases accordingly. On the contrary, GFM control adopted by SVGs can effectively eliminate the negative damping region in the high-frequency band for wind farms to suppress high-frequency resonance. Meanwhile, for grid-forming SVGs, the parameter variations in power synchronous loops have no significant impact on the suppressing effect of high-frequency resonance for wind farms. Finally, an electromagnetic simulation model for a DFIG-based wind farm system with an SVG is established using the StarSim-HIL (hardware-in-the-loop) experiment platform, and the simulation results validate the correctness of the theoretical analysis.

Ergonomic Studies on Occupational Health of Women Workers Involved in Agricultural Industries: A Systematic Review

This study undertakes a systematic review focusing on occupational health hazards and related issues experienced by women in the agriculture sector. Women engage in diverse and demanding farm operations, involving substantial energy expenditure, prolonged hours, and awkward postures, contributing to both drudgery and occupational health risks. Understanding the root causes of these conditions and establishing effective measurement methods is crucial. Moreover, identifying strategies to mitigate these hazards is imperative. A comprehensive literature review on women in agriculture illuminates various health concerns, their origins, analytical approaches, and potential enhancements to working conditions. To conduct a systematic review, the study adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, incorporating 79 relevant articles from Scopus and PubMed, following strict inclusion and exclusion criteria, published between 1989 and 2022. The findings revealed a concerning trend wherein female workers increasingly assume precarious, underpaid, and labour-intensive roles within the agricultural sector. Occupational health risks are heightened due to factors such as heat exposure, pesticide use, injuries, incontinence, awkward postures, and activities prone to drudgery. Ergonomically designed technology is pivotal in alleviating women’s occupational challenges, although this area remains relatively underexplored. To address these occupational hazards and health concerns effectively, there is a need for governmental intervention. Establishing a dedicated task force or committee is recommended. Furthermore, a multi-stakeholder network comprising government bodies, non-profit organizations, and civil society groups is proposed to enhance the well-being of women working in agriculture collaboratively. This study is the first of its kind that effectively highlights the multidimensional health issues women workers face, shedding light on their working conditions in the agriculture industry.

Study on the Design of Series-Type All-DC Wind Farms Based on Half-Bridge Voltage Balancing Circuits

Offshore wind farms connected in series, with each wind turbine connected in series with one another, enhance the coupling between them. Significant differences in wind speeds between neighboring DC wind turbines (DCWTs) might result in a substantial disparity in the output voltage, hence posing a risk of overvoltage. Nevertheless, implementing voltage-limiting configurations for DCWTs might lead to the dissipation of wind energy, thereby diminishing the wind farm’s capacity to deliver electricity. This work introduces a half-bridge voltage balancing circuit (HVBC) topology as a solution to the issue of DCWT output voltage changes affecting the stable operation of wind farms. The proposed HVBC topology is designed specifically for large-capacity series-connected all-DC wind farms where wind speed variations occur. This design achieves power decoupling for series-connected all-DC wind farms by providing current compensation to the series-connected DCWTs. A control strategy is devised by examining the decoupling principle and operational characteristics of the HVBC. A 60 kV/48 MW tandem-type all-DC wind farm model consisting of six DCWTs in series is built in Matlab/Simulink. The model is then simulated to evaluate its performance under conditions of unequal wind speed, rapid changes in wind speed, and wind turbine failure shutdown. This research verifies the feasibility of the HVBC topology and improves the stability of the series-type all-DC wind farm.

Medium-term economic impacts of cover crop adoption in Maryland

Medium-term economic impacts of cover crop adoption in Maryland

Enhanced Systemic and Mucosal Immune Responses to Haemophilus parasuis by Intranasal Administration of Lactic-Co-Glycolic Acid Microspheres.

Swine Glasser's disease, instigated by Haemophilus parasuis (H. parasuis), is a significant bacterial infection that causes substantial economic losses in pig farming operations. The role of mucosal immunity is pivotal in defending against H. parasuis. This study focused on the construction of PLGA microspheres that encapsulate the outer membrane protein OMP16 from H. parasuis (PLGA-OMP16) and evaluated their immunological effectiveness in a mouse model. After being intranasally immunized twice, the PLGA-OMP16 microspheres effectively induced IgAs in saliva and nasal and lung fluids. The PLGA-OMP16 microspheres also significantly increased the number of anti H. parasuis IgGs in serum. Furthermore, the PLGA-OMP16 microspheres triggered elevated levels of IL-2, IL-4, and IFN-γ. The mice vaccinated with PLGA-OMP16 showed a significant reduction in H. parasuis burden in the spleen and lungs following bacterial challenge. These results indicate that intranasal immunization using PLGA microspheres is a promising adjuvant delivery system for vaccines targeting H. parasuis.

A semi-quantitative risk model for dairy farms to pinpoint and break source-pathway connections between nutrient sources and open drainage channel sections

IntroductionOn intensive grassland dairy farms in high rainfall areas with poorly drained soils, networks of open drainage channels linked to in-field drainage systems are needed to enable farm operations. Nitrogen and phosphorus point and diffuse sources may be connected to this open drainage channel network along surface and subsurface pathways, with negative impacts upon delivery to the downstream aquatic system.MethodsThis study developed a semi-quantitative risk assessment model by: (1) selecting parameters (categorical or continuous) representing the nutrient transfer continuum and (2) scoring (relative magnitude and impact) the risk of nutrient source connectivity and delivery for every open drainage channel section across seven dairy farms.Results and DiscussionA Risk Index Classification System consisting of low, medium, high, or very high-risk class was developed, with high or above requiring a mitigation plan. Results showed that 23%, 68%, 9% and 0% of all open drainage channels on study farms were identified as low, moderate, high and very high-risk, respectively. A range from 2% to 25% per farm of the open drainage channels was classified as high-risk that potentially needed mitigation, although none was identified as very high-risk. Two-thirds of the high-risk open drainage channels were connected to the farmyards, with potential for high nutrient loss from point sources. A combined approach of source management and targeted breaking of the pathway (e.g., in-channel filters, water diversion bars) may help minimise nutrient losses from high risk open drainage channels on poorly draining soils.

Vulnerability and adaptation on the icecap’s edge: farming communities in subarctic South Greenland

ABSTRACT Variability in climate and weather conditions and socio-economic shifts are dramatically altering the landscapes and livelihoods of peoples of the northern high-latitudes. Such escalating trends have distinctive consequences at the local and regional scale and the ability to adapt to such rapid changes will challenge cultural, social, and economic systems across Arctic regions. Using a cultural landscape approach, this study provides an in-depth qualitative analysis of sheep farming communities in subarctic South Greenland. Our study centers local voices and helps document shifting weather-related and other socio-economic challenges. We interview four farming households, one historian, and one local economic development expert to understand the broader context, the challenges and vulnerabilities, and the persistence of agriculture in this high north region. Our analysis reveals four overarching themes influencing the sustainability of sheep farming: (1) multi-generational farming heritage, (2) reciprocity and community support, (3) shifting weather and climate influences and farming livelihoods, and (4) farm operations and new opportunities. Findings from this study fill a gap in the literature on local and regional impacts of weather and socio-economic shifts in remote regions. Through the inclusion of local Inuit and other voices, we document how geographically dispersed subarctic communities are adapting to broader shifts.

Tomato dry farming as an agroecological model for California’s drought resilient future: Farmers’ perspectives and experiences

Small, diversified farms on California’s Central Coast have been dry farming for decades, allowing farmers to use water stored in soils from winter rains to grow tomatoes and other vegetables with little-to-no irrigation in summers without rainfall. As recent water shortages in California have forced a reckoning with the precariousness of the state’s water supply, policy groups and the general public have become increasingly interested in dry farming as a promising means of achieving water conservation goals. Academic research on this practice, however, has been scarce. Amid growing urgency to develop low-water agricultural systems in the state, we interviewed 10 Central Coast dry farmers, representing over half of the commercial dry farm operations in the region where this practice was developed, to collaboratively answer 2 central research questions: (1) What business and land stewardship practices characterize successful tomato dry farming on California’s Central Coast? (2) What is the potential for dry farming to expand beyond its current adoption while maintaining its identity as a diversified practice that benefits small-scale operations? We summarize farmers’ wisdom into 9 themes about current dry farm practice, its potential for expansion and future opportunities. We also synthesize farmer-stated environmental constraints on dry farm feasibility into a map of suitable areas in California. As we consider how dry farming might expand to new areas and operations, we highlight dry farming’s history as an agroecological alternative to industrial farming in the region and the need for careful policy planning to maintain that identity. In examining this California Central Coast dry farming system, we ask if and how it can enhance the viability of nonindustrial farming operations as the food system adapts to less water availability. Because policies that encourage dry farm expansion could change economic landscapes in which dry farming operates, we caution that well-intentioned policies could edge small growers out of dry farm markets if not carefully designed. At the same time, we emphasize the opportunity for dry farm tomato systems to model an agroecological transition toward water savings in California.

The Wind Power Watch, "Guarding" for Safety - Data-Driven Intelligent Deterrence System for Monitoring Ships in Offshore Wind Farms

This study successfully developed a ship monitoring and repulsion system for offshore wind farms aimed at ensuring the safety and stable operation of the wind farms. The system is capable of real-time monitoring of ship activities and environmental conditions, quickly identifying and eliminating potential safety risks. The research content includes various aspects such as ship detection, path tracking, repulsion decision-making, and data processing. The system integrates multi-sensor data, Geographic Information Systems (GIS), Automatic Identification Systems (AIS) for ships, artificial intelligence algorithms, and advanced communication technologies, constructing an efficient and intelligent monitoring and repulsion framework. Field test results indicate that the system exhibits stable performance and efficient monitoring capabilities under harsh weather conditions, while the intelligent voice repulsion function has shown significant effects.

Inhibition of fish rhabdovirus demonstrates application prospect of two methylimidazole phenylpropanoid-based small molecules in aquaculture

Inhibition of fish rhabdovirus demonstrates application prospect of two methylimidazole phenylpropanoid-based small molecules in aquaculture

Integrated Farm Management System for Smart Agriculture in Oman

Abstract: Innovation became an utmost word to deal with in this hi-tech modern world. From a kitchen to parliament, agriculture to semiconductor industry, barbershop to superstore everywhere innovation taken place. Implementation of innovative idea in the relevant area or business field became a magnificent fashion. Therefore, the purpose of this paper shall be to build a solid on this issue. visionary technology advancement for Oman agriculture sector that will also benefit and when implemented, as an innovative solution to support agriculture as an icon for global development. The suggested Farm Management System utilize IoT to gather live information. related information of the soil, temperature and humidity conditions, crop yield, and stock so as to make a better. informed decision-making. Its user-friendly interface provides foolproof recommendations that assist farmers to enhance on production and sustainability. courses it comprises a transportation management, demand forecasting, production forecasting, inventory control, and inventory purchasing. module that help in tracking of the resources and also help in organizing the farm operations. This overall approach does not only enhance productivity but it also revamps the agriculture so much that it empowers the farmers. with the capabilities that will enable them to succeed in a dynamic environment within the sector.

An Improved Instance Segmentation Method for Complex Elements of Farm UAV Aerial Survey Images.

Farm aerial survey layers can assist in unmanned farm operations, such as planning paths and early warnings. To address the inefficiencies and high costs associated with traditional layer construction, this study proposes a high-precision instance segmentation algorithm based on SparseInst. Considering the structural characteristics of farm elements, this study introduces a multi-scale attention module (MSA) that leverages the properties of atrous convolution to expand the sensory field. It enhances spatial and channel feature weights, effectively improving segmentation accuracy for large-scale and complex targets in the farm through three parallel dense connections. A bottom-up aggregation path is added to the feature pyramid fusion network, enhancing the model's ability to perceive complex targets such as mechanized trails in farms. Coordinate attention blocks (CAs) are incorporated into the neck to capture richer contextual semantic information, enhancing farm aerial imagery scene recognition accuracy. To assess the proposed method, we compare it against existing mainstream object segmentation models, including the Mask R-CNN, Cascade-Mask, SOLOv2, and Condinst algorithms. The experimental results show that the improved model proposed in this study can be adapted to segment various complex targets in farms. The accuracy of the improved SparseInst model greatly exceeds that of Mask R-CNN and Cascade-Mask and is 10.8 and 12.8 percentage points better than the average accuracy of SOLOv2 and Condinst, respectively, with the smallest number of model parameters. The results show that the model can be used for real-time segmentation of targets under complex farm conditions.

ANALYSIS OF UTILIZATION OF ANIMAL TRACTION POWER BY CROP FARMERS IN JIGAWA STATE, NIGERIA

The use of animals for farm operations lighten human labour for farm cultivation, freight and transport thereby contributing immensely to the farm economy. The study was undertaken to analyse utilization of animal traction power by farmers in Jigawa State, Nigeria. Multistage sampling technique was employed to select 266 respondents from a total population of 3,3895 registered farmers using animal traction power. Information were collected using a structured questionnaire and interview schedule. Data collected were analyzed using descriptive and inferential statistics. The results revealed that all of the respondents were adult, married males with mean age of 25 years. A little over one-third of the respondents are literate with significant (75%) membership of associations. Also, 30.0% and 23.3% of the respondents have experience of animal traction power utilization of between 11-15 years, and above 20 years, respectively while 43% have farm size of not more than half (0.5) hectare. Over 60% inherited the farmlands. The study revealed that about 84% of the respondent’ source of animal traction power are bulls. Tillage practices (44.41%), transportation (21.27%) and pulling sledges (15.06%) ranked first, second and third respectively, as the major farming operations traction power is employed for in the study area. Multiple regression analysis results shows that education level, farming experience and farm size had a positive and significant relationship affecting and influencing use animal traction by the respondents. Also, over one-third (38.8%) of the respondents were challenged by scarcity of feed resources and water while 20.3% were constrained by animal health issues. Therefore, the study concluded that animal traction power is a reliable means of farming operation utilized by small scale farmers that has the potential of uplifting the socio-economics standard of the farmers.

A Mobile based Decision Support System for Postural Evaluation of Agricultural Activities with Rapid Entire Body Assessment (REBA)

Agriculture is an unorganized sector and it is a very high drudgery prone profession due to the absence of awareness and entry of advanced agricultural technologies. Many farming activities and operations are carried out by farm workers in the agriculture sector, where they are subject to very poor working conditions which can lead to physical and mental stress. Workers in agriculture experience musculoskeletal disorders in various body parts throughout various agricultural operations. These problems are caused by heavy work loads, repetitive movements, uncomfortable postures, prolonged periods of time spent in neutral or unsupported positions and the use of conventional equipment and implements that are not ergonomically built. The ergonomic risk factors present in the workplace can be examined using various evaluation instruments to ascertain the workers abilities and drawbacks. The work-related risks are measured using the scientifically validated posture assessment instruments. Rapid entire body assessment (REBA) is a technique that assess a person’s complete body including wrists, fore arms, elbows, shoulders, neck, trunk, back, legs and knees. It uses a systematic process to find the postural musculoskeletal disorders and risks associated with the job tasks. A mobile application is developed to assess the agricultural activities on the basis of physiological analysis and postural analysis using REBA ergonomics technique. The application evaluates the drudgery involved in the agricultural work by using physical assessment followed by postural ergonomics methods and provide recommendation of correct posture to avoid developing musculoskeletal disorders.